
# Installing the libraries with the specified version.
!pip install pandas==1.5.3 numpy==1.25.2 matplotlib==3.7.1 seaborn==0.13.1 scikit-learn==1.2.2 statsmodels==0.14.1 -q --user


#Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

#Libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

import warnings
warnings.filterwarnings("ignore")

#Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
#Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
#Setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)

#Library to split data
from sklearn.model_selection import train_test_split

#To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

#To tune different models
from sklearn.model_selection import GridSearchCV

# To get diferent metric scores
from sklearn.metrics import (
    f1_score,  
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    precision_recall_curve,
    roc_curve,
    make_scorer,
)


#import csv into dataframe
hotel = pd.read_csv("INNHotelsGroup.csv")


# copying data to another varaible to avoid any changes to original data
df = hotel.copy()


#Show first five rows of data
df.head()


#Last 5 rows of the dataset

df.tail()


df.shape

(36275, 19)


#show a random sample of five rows of data
df.sample(n=5)


# drop the 'Booking ID' columns from the data set. 
df = df.drop('Booking_ID', axis=1)


# view what are the values in object data types
cat_columns = ['type_of_meal_plan', 'room_type_reserved', 'market_segment_type', 'booking_status']
for i in cat_columns:
    print(df[i].value_counts())
    print("*" * 50)

Meal Plan 1     27835
Not Selected     5130
Meal Plan 2      3305
Meal Plan 3         5
Name: type_of_meal_plan, dtype: int64
**************************************************
Room_Type 1    28130
Room_Type 4     6057
Room_Type 6      966
Room_Type 2      692
Room_Type 5      265
Room_Type 7      158
Room_Type 3        7
Name: room_type_reserved, dtype: int64
**************************************************
Online           23214
Offline          10528
Corporate         2017
Complementary      391
Aviation           125
Name: market_segment_type, dtype: int64
**************************************************
Not_Canceled    24390
Canceled        11885
Name: booking_status, dtype: int64
**************************************************


df.describe()


df.describe(include = ['object'])


# function to create labeled barplots

# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


# function to plot a boxplot and a histogram along the same scale.


def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


def stacked_barplot(data, predictor, target, perc=False):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
  
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5,))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


labeled_barplot(df, 'arrival_month', perc=True, n=None)


labeled_barplot(df, 'market_segment_type', perc=True, n=None)


histogram_boxplot(df, 'avg_price_per_room')


# how many free rooms does the hotel give away?
df[df['avg_price_per_room']==0]


df.loc[df['avg_price_per_room']==0, 'market_segment_type'].value_counts()

Complementary    354
Online           191
Name: market_segment_type, dtype: int64


labeled_barplot(df, 'booking_status', perc=True, n=None)


sns.boxplot(data=df, y='avg_price_per_room' , x='market_segment_type')

<Axes: xlabel='market_segment_type', ylabel='avg_price_per_room'>


stacked_barplot(df,'repeated_guest','booking_status')

booking_status  Canceled  Not_Canceled    All
repeated_guest                               
All                11885         24390  36275
0                  11869         23476  35345
1                     16           914    930
------------------------------------------------------------------------------------------------------------------------


sns.catplot(data=df, y='no_of_special_requests', hue='booking_status', kind='count' )

<seaborn.axisgrid.FacetGrid at 0x7fb82b53c6d0>


#group by *arrival month*, count number of records per month, sort from most to fewest bookings, and show top 4 months
df.groupby('arrival_month').count().sort_values(by='booking_status', ascending=False)['booking_status'].head(4)

arrival_month
10    5317
9     4611
8     3813
6     3203
Name: booking_status, dtype: int64


df.groupby('market_segment_type').count().sort_values(by='booking_status', ascending=False)['booking_status']

market_segment_type
Online           23214
Offline          10528
Corporate         2017
Complementary      391
Aviation           125
Name: booking_status, dtype: int64


df.groupby('market_segment_type').agg({'avg_price_per_room':'mean'}).sort_values(by='avg_price_per_room',ascending=False)


df['booking_status'].value_counts()

Not_Canceled    24390
Canceled        11885
Name: booking_status, dtype: int64


df.groupby('repeated_guest')['booking_status'].value_counts()

repeated_guest  booking_status
0               Not_Canceled      23476
                Canceled          11869
1               Not_Canceled        914
                Canceled             16
Name: booking_status, dtype: int64


sns.countplot(data=df, hue='booking_status', x='no_of_special_requests')
plt.legend(loc='upper right')
plt.show()


labeled_barplot(df, 'no_of_weekend_nights', perc=True)


labeled_barplot(df, 'no_of_adults', perc=True)


labeled_barplot(df, 'no_of_children', perc=True)


labeled_barplot(df, 'required_car_parking_space', perc=True)


labeled_barplot(df, 'no_of_week_nights', perc=True)


labeled_barplot(df, 'type_of_meal_plan', perc=True)


labeled_barplot(df, 'room_type_reserved', perc=True)


histogram_boxplot(df, 'no_of_previous_cancellations')


labeled_barplot(df, 'lead_time', perc=True)


plt.figure(figsize=(20,10))
sns.heatmap(
df.corr(), annot=True, vmin=-1, vmax=1, fmt='.2f')

<Axes: >


# New column for lenght of stay
df['length_stay'] = df['no_of_weekend_nights'] + df['no_of_week_nights']


sns.pairplot(df[['no_of_weekend_nights','no_of_week_nights','required_car_parking_space',
                   'lead_time','avg_price_per_room','no_of_special_requests','type_of_meal_plan',
                   'room_type_reserved','market_segment_type','booking_status','length_stay']]);


df.loc[df['booking_status']=='Not_Canceled','booking_status'] = False
df.loc[df['booking_status']=='Canceled','booking_status'] = True


numeric_columns = df.select_dtypes(include=np.number).columns.tolist()
# drop column because they were either time, or not helpful
numeric_columns.remove("arrival_year")


plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    df.boxplot() 

plt.xticks(rotation=45)
plt.show()


#Solving the IQR fro avg price room
quartiles = np.quantile(df['avg_price_per_room'][df['avg_price_per_room'].notnull()], [.25, .75])
power_4iqr = 4 * (quartiles[1] - quartiles[0])
print(f'Q1 = {quartiles[0]}, Q3 = {quartiles[1]}, 4*IQR = {power_4iqr}')
outlier_powers = df.loc[np.abs(df['avg_price_per_room'] - df['avg_price_per_room'].median()) > power_4iqr, 'avg_price_per_room']
outlier_powers.shape

Q1 = 80.3, Q3 = 120.0, 4*IQR = 158.8

(49,)


# creating a list of columns
dist_cols = [
    item for item in df.select_dtypes(include=np.number).columns 
]

plt.figure(figsize=(15, 45))
#looping the list and ploting histograms
for i in range(len(dist_cols)):
    plt.subplot(12, 3, i + 1)
    plt.hist(df[dist_cols[i]], bins=50)
    plt.tight_layout()
    plt.title(dist_cols[i], fontsize=15)

plt.show()


df2 = df.copy()
# removing because they are close to normal
dist_cols.remove('no_of_week_nights')
dist_cols.remove('no_of_adults')
dist_cols.remove('length_stay')
dist_cols.remove('avg_price_per_room')
# removing becasue they are boolean or time related. 
dist_cols.remove('arrival_year')
dist_cols.remove('required_car_parking_space')
dist_cols.remove('arrival_date')
dist_cols.remove('arrival_month')
dist_cols.remove('repeated_guest')
# removing becasue I have a different treatment in mind
dist_cols.remove('lead_time')


# using log transforms on some columns
for col in dist_cols:
    df2[col + "_log"] = np.log(df2[col] + 1)

# dropping the original columns
df2.drop(dist_cols, axis=1, inplace=True)
df2.head()


# viewing the distributions after the log transformation. 
dist_cols = [
    item for item in df2.select_dtypes(include=np.number).columns 
]

#  plot histogram of all numeric columns

plt.figure(figsize=(15, 45))

for i in range(len(dist_cols)):
    plt.subplot(12, 3, i + 1)
    plt.hist(df2[dist_cols[i]], bins=50)
    sns.histplot(data=df2, x=dist_cols[i], kde=True)  
    plt.tight_layout()
    plt.title(dist_cols[i], fontsize=25)

plt.show()


dummy_data = pd.get_dummies (
    df2,
    columns = [
        'type_of_meal_plan',
        'room_type_reserved',
        'market_segment_type',
    ],
    drop_first=True,
)
dummy_data.head()


dummy_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 29 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              36275 non-null  int64  
 1   no_of_week_nights                         36275 non-null  int64  
 2   required_car_parking_space                36275 non-null  int64  
 3   lead_time                                 36275 non-null  int64  
 4   arrival_year                              36275 non-null  int64  
 5   arrival_month                             36275 non-null  int64  
 6   arrival_date                              36275 non-null  int64  
 7   repeated_guest                            36275 non-null  int64  
 8   avg_price_per_room                        36275 non-null  float64
 9   booking_status                            36275 non-null  object 
 10  length_stay                               36275 non-null  int64  
 11  no_of_children_log                        36275 non-null  float64
 12  no_of_weekend_nights_log                  36275 non-null  float64
 13  no_of_previous_cancellations_log          36275 non-null  float64
 14  no_of_previous_bookings_not_canceled_log  36275 non-null  float64
 15  no_of_special_requests_log                36275 non-null  float64
 16  type_of_meal_plan_Meal Plan 2             36275 non-null  uint8  
 17  type_of_meal_plan_Meal Plan 3             36275 non-null  uint8  
 18  type_of_meal_plan_Not Selected            36275 non-null  uint8  
 19  room_type_reserved_Room_Type 2            36275 non-null  uint8  
 20  room_type_reserved_Room_Type 3            36275 non-null  uint8  
 21  room_type_reserved_Room_Type 4            36275 non-null  uint8  
 22  room_type_reserved_Room_Type 5            36275 non-null  uint8  
 23  room_type_reserved_Room_Type 6            36275 non-null  uint8  
 24  room_type_reserved_Room_Type 7            36275 non-null  uint8  
 25  market_segment_type_Complementary         36275 non-null  uint8  
 26  market_segment_type_Corporate             36275 non-null  uint8  
 27  market_segment_type_Offline               36275 non-null  uint8  
 28  market_segment_type_Online                36275 non-null  uint8  
dtypes: float64(6), int64(9), object(1), uint8(13)
memory usage: 4.9+ MB


dummied_cut = pd.cut(dummy_data['lead_time'], 5, labels=['lat_min','short','med','long','advanced'])
dummied_cut.head(10)

0        med
1    lat_min
2    lat_min
3        med
4    lat_min
5       long
6    lat_min
7    lat_min
8      short
9    lat_min
Name: lead_time, dtype: category
Categories (5, object): ['lat_min' < 'short' < 'med' < 'long' < 'advanced']


df3 = pd.merge(dummy_data, dummied_cut, left_index=True, right_index=True)

df3.head().T


# dropping time variables and lead_time_x since it has been binned into 5 columns.

df3_5 = df3.drop(['lead_time_x','arrival_date', 'arrival_year'], axis=1)


df4 = pd.get_dummies (
    df3_5,
    columns = [
        'lead_time_y',  
    ],
    drop_first=True,
)
df4.head().T


df4 = df4.astype(float)


df4.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 30 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              36275 non-null  float64
 1   no_of_week_nights                         36275 non-null  float64
 2   required_car_parking_space                36275 non-null  float64
 3   arrival_month                             36275 non-null  float64
 4   repeated_guest                            36275 non-null  float64
 5   avg_price_per_room                        36275 non-null  float64
 6   booking_status                            36275 non-null  float64
 7   length_stay                               36275 non-null  float64
 8   no_of_children_log                        36275 non-null  float64
 9   no_of_weekend_nights_log                  36275 non-null  float64
 10  no_of_previous_cancellations_log          36275 non-null  float64
 11  no_of_previous_bookings_not_canceled_log  36275 non-null  float64
 12  no_of_special_requests_log                36275 non-null  float64
 13  type_of_meal_plan_Meal Plan 2             36275 non-null  float64
 14  type_of_meal_plan_Meal Plan 3             36275 non-null  float64
 15  type_of_meal_plan_Not Selected            36275 non-null  float64
 16  room_type_reserved_Room_Type 2            36275 non-null  float64
 17  room_type_reserved_Room_Type 3            36275 non-null  float64
 18  room_type_reserved_Room_Type 4            36275 non-null  float64
 19  room_type_reserved_Room_Type 5            36275 non-null  float64
 20  room_type_reserved_Room_Type 6            36275 non-null  float64
 21  room_type_reserved_Room_Type 7            36275 non-null  float64
 22  market_segment_type_Complementary         36275 non-null  float64
 23  market_segment_type_Corporate             36275 non-null  float64
 24  market_segment_type_Offline               36275 non-null  float64
 25  market_segment_type_Online                36275 non-null  float64
 26  lead_time_y_short                         36275 non-null  float64
 27  lead_time_y_med                           36275 non-null  float64
 28  lead_time_y_long                          36275 non-null  float64
 29  lead_time_y_advanced                      36275 non-null  float64
dtypes: float64(30)
memory usage: 8.3 MB


# Using the SCIEM method I will split the train test data first.
X = df4.drop("booking_status" , axis=1)
y = df4.pop("booking_status")

# adding a contstant to X variable
X = add_constant(X)


# Train/Test Split 70/30 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.30, random_state=1)


print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in test data =", X_test.shape[0])

Number of rows in train data = 25392
Number of rows in test data = 10883


print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Percentage of classes in training set:
0.00000   0.67064
1.00000   0.32936
Name: booking_status, dtype: float64
Percentage of classes in test set:
0.00000   0.67638
1.00000   0.32362
Name: booking_status, dtype: float64


X_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25392 entries, 13662 to 33003
Data columns (total 30 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   const                                     25392 non-null  float64
 1   no_of_adults                              25392 non-null  float64
 2   no_of_week_nights                         25392 non-null  float64
 3   required_car_parking_space                25392 non-null  float64
 4   arrival_month                             25392 non-null  float64
 5   repeated_guest                            25392 non-null  float64
 6   avg_price_per_room                        25392 non-null  float64
 7   length_stay                               25392 non-null  float64
 8   no_of_children_log                        25392 non-null  float64
 9   no_of_weekend_nights_log                  25392 non-null  float64
 10  no_of_previous_cancellations_log          25392 non-null  float64
 11  no_of_previous_bookings_not_canceled_log  25392 non-null  float64
 12  no_of_special_requests_log                25392 non-null  float64
 13  type_of_meal_plan_Meal Plan 2             25392 non-null  float64
 14  type_of_meal_plan_Meal Plan 3             25392 non-null  float64
 15  type_of_meal_plan_Not Selected            25392 non-null  float64
 16  room_type_reserved_Room_Type 2            25392 non-null  float64
 17  room_type_reserved_Room_Type 3            25392 non-null  float64
 18  room_type_reserved_Room_Type 4            25392 non-null  float64
 19  room_type_reserved_Room_Type 5            25392 non-null  float64
 20  room_type_reserved_Room_Type 6            25392 non-null  float64
 21  room_type_reserved_Room_Type 7            25392 non-null  float64
 22  market_segment_type_Complementary         25392 non-null  float64
 23  market_segment_type_Corporate             25392 non-null  float64
 24  market_segment_type_Offline               25392 non-null  float64
 25  market_segment_type_Online                25392 non-null  float64
 26  lead_time_y_short                         25392 non-null  float64
 27  lead_time_y_med                           25392 non-null  float64
 28  lead_time_y_long                          25392 non-null  float64
 29  lead_time_y_advanced                      25392 non-null  float64
dtypes: float64(30)
memory usage: 6.0 MB


# let's check the VIF of the predictors
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                      326.14192
no_of_adults                                 1.34666
no_of_week_nights                          100.27746
required_car_parking_space                   1.04158
arrival_month                                1.05151
repeated_guest                               3.34004
avg_price_per_room                           1.93604
length_stay                                146.44254
no_of_children_log                           1.86632
no_of_weekend_nights_log                    34.42876
no_of_previous_cancellations_log             1.59714
no_of_previous_bookings_not_canceled_log     3.50891
no_of_special_requests_log                   1.26796
type_of_meal_plan_Meal Plan 2                1.21752
type_of_meal_plan_Meal Plan 3                1.02532
type_of_meal_plan_Not Selected               1.23653
room_type_reserved_Room_Type 2               1.09067
room_type_reserved_Room_Type 3               1.00338
room_type_reserved_Room_Type 4               1.36465
room_type_reserved_Room_Type 5               1.02802
room_type_reserved_Room_Type 6               1.85857
room_type_reserved_Room_Type 7               1.11100
market_segment_type_Complementary            4.50724
market_segment_type_Corporate               16.93002
market_segment_type_Offline                 64.01666
market_segment_type_Online                  71.24829
lead_time_y_short                            1.11927
lead_time_y_med                              1.10532
lead_time_y_long                             1.15163
lead_time_y_advanced                         1.04712
dtype: float64


#dropping the number of weekend & week nights because I have combined them into one & market segements because they all have large multi values
X_train1 = X_train.drop(['no_of_weekend_nights_log',
                         'no_of_week_nights',
                         'market_segment_type_Online',
                         'market_segment_type_Offline',
                         'market_segment_type_Corporate',
                        'market_segment_type_Complementary'],
                       axis=1)


logit = sm.Logit(y_train, X_train1.astype(float))
lg = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463427
         Iterations 10


# Print the logistic regression summary
print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25368
Method:                           MLE   Df Model:                           23
Date:                Sun, 14 Jul 2024   Pseudo R-squ.:                  0.2687
Time:                        11:01:24   Log-Likelihood:                -11767.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
============================================================================================================
                                               coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------------------
const                                       -3.6818      0.098    -37.650      0.000      -3.873      -3.490
no_of_adults                                 0.2321      0.035      6.614      0.000       0.163       0.301
required_car_parking_space                  -1.4537      0.135    -10.742      0.000      -1.719      -1.188
arrival_month                               -0.0668      0.006    -11.685      0.000      -0.078      -0.056
repeated_guest                              -2.6424      0.630     -4.193      0.000      -3.878      -1.407
avg_price_per_room                           0.0229      0.001     33.788      0.000       0.022       0.024
length_stay                                  0.1088      0.009     11.946      0.000       0.091       0.127
no_of_children_log                           0.5488      0.093      5.887      0.000       0.366       0.732
no_of_previous_cancellations_log             1.2323      0.490      2.515      0.012       0.272       2.193
no_of_previous_bookings_not_canceled_log    -0.6731      0.477     -1.411      0.158      -1.608       0.262
no_of_special_requests_log                  -1.9180      0.044    -43.892      0.000      -2.004      -1.832
type_of_meal_plan_Meal Plan 2               -0.3480      0.056     -6.165      0.000      -0.459      -0.237
type_of_meal_plan_Meal Plan 3                1.7182      2.912      0.590      0.555      -3.989       7.425
type_of_meal_plan_Not Selected               0.8463      0.048     17.563      0.000       0.752       0.941
room_type_reserved_Room_Type 2               0.1288      0.123      1.045      0.296      -0.113       0.370
room_type_reserved_Room_Type 3              -0.2278      1.194     -0.191      0.849      -2.567       2.111
room_type_reserved_Room_Type 4               0.0548      0.050      1.095      0.273      -0.043       0.153
room_type_reserved_Room_Type 5              -0.9272      0.196     -4.735      0.000      -1.311      -0.543
room_type_reserved_Room_Type 6              -1.0662      0.135     -7.903      0.000      -1.331      -0.802
room_type_reserved_Room_Type 7              -1.8078      0.286     -6.331      0.000      -2.368      -1.248
lead_time_y_short                            1.3167      0.039     34.051      0.000       1.241       1.393
lead_time_y_med                              2.8622      0.058     49.315      0.000       2.748       2.976
lead_time_y_long                             3.0529      0.077     39.428      0.000       2.901       3.205
lead_time_y_advanced                         4.5673      0.247     18.478      0.000       4.083       5.052
============================================================================================================


# Checking the VIF of the predictors
vif_series = pd.Series(
    [variance_inflation_factor(X_train1.values, i) for i in range(X_train1.shape[1])],
    index=X_train1.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                      29.38943
no_of_adults                                1.27904
required_car_parking_space                  1.03734
arrival_month                               1.04591
repeated_guest                              3.21685
avg_price_per_room                          1.58350
length_stay                                 1.07689
no_of_children_log                          1.85549
no_of_previous_cancellations_log            1.57679
no_of_previous_bookings_not_canceled_log    3.44533
no_of_special_requests_log                  1.13382
type_of_meal_plan_Meal Plan 2               1.13453
type_of_meal_plan_Meal Plan 3               1.01864
type_of_meal_plan_Not Selected              1.10882
room_type_reserved_Room_Type 2              1.07952
room_type_reserved_Room_Type 3              1.00088
room_type_reserved_Room_Type 4              1.31700
room_type_reserved_Room_Type 5              1.01351
room_type_reserved_Room_Type 6              1.83383
room_type_reserved_Room_Type 7              1.07227
lead_time_y_short                           1.10534
lead_time_y_med                             1.09193
lead_time_y_long                            1.12297
lead_time_y_advanced                        1.04374
dtype: float64


# Test performance
pred_train = lg.predict(X_train1) > 0.5
pred_train = np.round(pred_train)


X_train2 = X_train1.drop(['room_type_reserved_Room_Type 3'], axis=1)
X_train2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25392 entries, 13662 to 33003
Data columns (total 23 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   const                                     25392 non-null  float64
 1   no_of_adults                              25392 non-null  float64
 2   required_car_parking_space                25392 non-null  float64
 3   arrival_month                             25392 non-null  float64
 4   repeated_guest                            25392 non-null  float64
 5   avg_price_per_room                        25392 non-null  float64
 6   length_stay                               25392 non-null  float64
 7   no_of_children_log                        25392 non-null  float64
 8   no_of_previous_cancellations_log          25392 non-null  float64
 9   no_of_previous_bookings_not_canceled_log  25392 non-null  float64
 10  no_of_special_requests_log                25392 non-null  float64
 11  type_of_meal_plan_Meal Plan 2             25392 non-null  float64
 12  type_of_meal_plan_Meal Plan 3             25392 non-null  float64
 13  type_of_meal_plan_Not Selected            25392 non-null  float64
 14  room_type_reserved_Room_Type 2            25392 non-null  float64
 15  room_type_reserved_Room_Type 4            25392 non-null  float64
 16  room_type_reserved_Room_Type 5            25392 non-null  float64
 17  room_type_reserved_Room_Type 6            25392 non-null  float64
 18  room_type_reserved_Room_Type 7            25392 non-null  float64
 19  lead_time_y_short                         25392 non-null  float64
 20  lead_time_y_med                           25392 non-null  float64
 21  lead_time_y_long                          25392 non-null  float64
 22  lead_time_y_advanced                      25392 non-null  float64
dtypes: float64(23)
memory usage: 4.6 MB


logit = sm.Logit(y_train, X_train2.astype(float))
lg2 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463428
         Iterations 10


print(lg2.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25369
Method:                           MLE   Df Model:                           22
Date:                Sun, 14 Jul 2024   Pseudo R-squ.:                  0.2687
Time:                        11:02:52   Log-Likelihood:                -11767.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
============================================================================================================
                                               coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------------------
const                                       -3.6818      0.098    -37.651      0.000      -3.873      -3.490
no_of_adults                                 0.2321      0.035      6.614      0.000       0.163       0.301
required_car_parking_space                  -1.4536      0.135    -10.742      0.000      -1.719      -1.188
arrival_month                               -0.0668      0.006    -11.687      0.000      -0.078      -0.056
repeated_guest                              -2.6423      0.630     -4.193      0.000      -3.878      -1.407
avg_price_per_room                           0.0229      0.001     33.789      0.000       0.022       0.024
length_stay                                  0.1089      0.009     11.948      0.000       0.091       0.127
no_of_children_log                           0.5489      0.093      5.887      0.000       0.366       0.732
no_of_previous_cancellations_log             1.2322      0.490      2.515      0.012       0.272       2.193
no_of_previous_bookings_not_canceled_log    -0.6731      0.477     -1.411      0.158      -1.608       0.262
no_of_special_requests_log                  -1.9180      0.044    -43.892      0.000      -2.004      -1.832
type_of_meal_plan_Meal Plan 2               -0.3479      0.056     -6.163      0.000      -0.459      -0.237
type_of_meal_plan_Meal Plan 3                1.7183      2.912      0.590      0.555      -3.988       7.425
type_of_meal_plan_Not Selected               0.8463      0.048     17.564      0.000       0.752       0.941
room_type_reserved_Room_Type 2               0.1289      0.123      1.046      0.296      -0.113       0.371
room_type_reserved_Room_Type 4               0.0549      0.050      1.097      0.273      -0.043       0.153
room_type_reserved_Room_Type 5              -0.9271      0.196     -4.735      0.000      -1.311      -0.543
room_type_reserved_Room_Type 6              -1.0662      0.135     -7.903      0.000      -1.331      -0.802
room_type_reserved_Room_Type 7              -1.8078      0.286     -6.331      0.000      -2.367      -1.248
lead_time_y_short                            1.3166      0.039     34.051      0.000       1.241       1.392
lead_time_y_med                              2.8622      0.058     49.315      0.000       2.748       2.976
lead_time_y_long                             3.0529      0.077     39.429      0.000       2.901       3.205
lead_time_y_advanced                         4.5673      0.247     18.478      0.000       4.083       5.052
============================================================================================================


X_train3 = X_train2.drop(['no_of_previous_bookings_not_canceled_log'], axis=1)


logit = sm.Logit(y_train, X_train3.astype(float))
lg3 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463479
         Iterations 9


print(lg3.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25370
Method:                           MLE   Df Model:                           21
Date:                Sun, 14 Jul 2024   Pseudo R-squ.:                  0.2686
Time:                        11:03:36   Log-Likelihood:                -11769.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
const                               -3.6853      0.098    -37.686      0.000      -3.877      -3.494
no_of_adults                         0.2328      0.035      6.633      0.000       0.164       0.302
required_car_parking_space          -1.4531      0.135    -10.738      0.000      -1.718      -1.188
arrival_month                       -0.0667      0.006    -11.679      0.000      -0.078      -0.056
repeated_guest                      -2.9666      0.574     -5.169      0.000      -4.092      -1.842
avg_price_per_room                   0.0229      0.001     33.812      0.000       0.022       0.024
length_stay                          0.1089      0.009     11.954      0.000       0.091       0.127
no_of_children_log                   0.5492      0.093      5.890      0.000       0.366       0.732
no_of_previous_cancellations_log     0.9583      0.399      2.401      0.016       0.176       1.741
no_of_special_requests_log          -1.9193      0.044    -43.924      0.000      -2.005      -1.834
type_of_meal_plan_Meal Plan 2       -0.3491      0.056     -6.183      0.000      -0.460      -0.238
type_of_meal_plan_Meal Plan 3        1.7180      2.914      0.590      0.555      -3.992       7.429
type_of_meal_plan_Not Selected       0.8466      0.048     17.568      0.000       0.752       0.941
room_type_reserved_Room_Type 2       0.1290      0.123      1.046      0.296      -0.113       0.371
room_type_reserved_Room_Type 4       0.0543      0.050      1.085      0.278      -0.044       0.152
room_type_reserved_Room_Type 5      -0.9291      0.196     -4.746      0.000      -1.313      -0.545
room_type_reserved_Room_Type 6      -1.0676      0.135     -7.913      0.000      -1.332      -0.803
room_type_reserved_Room_Type 7      -1.8098      0.286     -6.337      0.000      -2.370      -1.250
lead_time_y_short                    1.3170      0.039     34.060      0.000       1.241       1.393
lead_time_y_med                      2.8638      0.058     49.342      0.000       2.750       2.978
lead_time_y_long                     3.0543      0.077     39.438      0.000       2.903       3.206
lead_time_y_advanced                 4.5896      0.248     18.494      0.000       4.103       5.076
====================================================================================================


# let's check the VIF of the predictors again to see if any Multicollinearity persist
vif_series = pd.Series(
    [variance_inflation_factor(X_train3.values, i) for i in range(X_train3.shape[1])],
    index=X_train3.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                              29.23951
no_of_adults                        1.27645
required_car_parking_space          1.03658
arrival_month                       1.04468
repeated_guest                      1.55246
avg_price_per_room                  1.57899
length_stay                         1.07664
no_of_children_log                  1.85538
no_of_previous_cancellations_log    1.42609
no_of_special_requests_log          1.12828
type_of_meal_plan_Meal Plan 2       1.13429
type_of_meal_plan_Meal Plan 3       1.01858
type_of_meal_plan_Not Selected      1.10868
room_type_reserved_Room_Type 2      1.07948
room_type_reserved_Room_Type 4      1.31640
room_type_reserved_Room_Type 5      1.01261
room_type_reserved_Room_Type 6      1.83272
room_type_reserved_Room_Type 7      1.07151
lead_time_y_short                   1.10526
lead_time_y_med                     1.09192
lead_time_y_long                    1.12291
lead_time_y_advanced                1.04351
dtype: float64


X_train4 = X_train3.drop(['room_type_reserved_Room_Type 2'], axis=1)


logit = sm.Logit(y_train, X_train4.astype(float))
lg4 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463500
         Iterations 9


print(lg4.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25371
Method:                           MLE   Df Model:                           20
Date:                Sun, 14 Jul 2024   Pseudo R-squ.:                  0.2686
Time:                        11:04:38   Log-Likelihood:                -11769.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
const                               -3.6755      0.097    -37.773      0.000      -3.866      -3.485
no_of_adults                         0.2313      0.035      6.592      0.000       0.162       0.300
required_car_parking_space          -1.4496      0.135    -10.723      0.000      -1.715      -1.185
arrival_month                       -0.0669      0.006    -11.722      0.000      -0.078      -0.056
repeated_guest                      -2.9693      0.574     -5.173      0.000      -4.094      -1.844
avg_price_per_room                   0.0229      0.001     33.816      0.000       0.022       0.024
length_stay                          0.1090      0.009     11.964      0.000       0.091       0.127
no_of_children_log                   0.5688      0.091      6.225      0.000       0.390       0.748
no_of_previous_cancellations_log     0.9574      0.399      2.398      0.016       0.175       1.740
no_of_special_requests_log          -1.9178      0.044    -43.917      0.000      -2.003      -1.832
type_of_meal_plan_Meal Plan 2       -0.3510      0.056     -6.220      0.000      -0.462      -0.240
type_of_meal_plan_Meal Plan 3        1.7193      2.911      0.591      0.555      -3.987       7.425
type_of_meal_plan_Not Selected       0.8443      0.048     17.541      0.000       0.750       0.939
room_type_reserved_Room_Type 4       0.0528      0.050      1.056      0.291      -0.045       0.151
room_type_reserved_Room_Type 5      -0.9320      0.196     -4.761      0.000      -1.316      -0.548
room_type_reserved_Room_Type 6      -1.0857      0.134     -8.110      0.000      -1.348      -0.823
room_type_reserved_Room_Type 7      -1.8191      0.286     -6.369      0.000      -2.379      -1.259
lead_time_y_short                    1.3172      0.039     34.067      0.000       1.241       1.393
lead_time_y_med                      2.8676      0.058     49.483      0.000       2.754       2.981
lead_time_y_long                     3.0549      0.077     39.444      0.000       2.903       3.207
lead_time_y_advanced                 4.5912      0.248     18.498      0.000       4.105       5.078
====================================================================================================


X_train5 = X_train4.drop(['room_type_reserved_Room_Type 4'], axis=1)


logit = sm.Logit(y_train, X_train5.astype(float))
lg5 = logit.fit()

Optimization terminated successfully.
         Current function value: 0.463522
         Iterations 9


print(lg5.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25372
Method:                           MLE   Df Model:                           19
Date:                Sun, 14 Jul 2024   Pseudo R-squ.:                  0.2686
Time:                        11:05:24   Log-Likelihood:                -11770.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
====================================================================================================
                                       coef    std err          z      P>|z|      [0.025      0.975]
----------------------------------------------------------------------------------------------------
const                               -3.7035      0.094    -39.515      0.000      -3.887      -3.520
no_of_adults                         0.2398      0.034      7.023      0.000       0.173       0.307
required_car_parking_space          -1.4500      0.135    -10.728      0.000      -1.715      -1.185
arrival_month                       -0.0672      0.006    -11.767      0.000      -0.078      -0.056
repeated_guest                      -2.9647      0.574     -5.163      0.000      -4.090      -1.839
avg_price_per_room                   0.0231      0.001     35.959      0.000       0.022       0.024
length_stay                          0.1100      0.009     12.154      0.000       0.092       0.128
no_of_children_log                   0.5594      0.091      6.150      0.000       0.381       0.738
no_of_previous_cancellations_log     0.9559      0.400      2.392      0.017       0.173       1.739
no_of_special_requests_log          -1.9157      0.044    -43.926      0.000      -2.001      -1.830
type_of_meal_plan_Meal Plan 2       -0.3601      0.056     -6.456      0.000      -0.469      -0.251
type_of_meal_plan_Meal Plan 3        1.7283      2.969      0.582      0.560      -4.090       7.547
type_of_meal_plan_Not Selected       0.8335      0.047     17.728      0.000       0.741       0.926
room_type_reserved_Room_Type 5      -0.9481      0.195     -4.858      0.000      -1.331      -0.566
room_type_reserved_Room_Type 6      -1.1095      0.132     -8.409      0.000      -1.368      -0.851
room_type_reserved_Room_Type 7      -1.8578      0.283     -6.556      0.000      -2.413      -1.302
lead_time_y_short                    1.3157      0.039     34.057      0.000       1.240       1.391
lead_time_y_med                      2.8630      0.058     49.547      0.000       2.750       2.976
lead_time_y_long                     3.0495      0.077     39.450      0.000       2.898       3.201
lead_time_y_advanced                 4.5867      0.248     18.479      0.000       4.100       5.073
====================================================================================================


# converting coefficients to odds
odds = np.exp(lg5.params)

# adding the odds to a dataframe
pd.DataFrame(odds, X_train5.columns, columns=["odds"]).T


# finding the percentage change
perc_change_odds = (np.exp(lg5.params) - 1) * 100

# adding the change_odds% to a dataframe
pd.DataFrame(perc_change_odds, X_train3.columns, columns=["change_odds%"]).T


# fitting the model on training set
logit = sm.Logit(y_train, X_train5.astype(float))
lg3 = logit.fit()

pred_train4 = lg5.predict(X_train5)
pred_train4 = np.round(pred_train4)

Optimization terminated successfully.
         Current function value: 0.463522
         Iterations 9


# another confusion matrix
cm = confusion_matrix(y_train, pred_train4)
plt.figure(figsize=(7, 5))
sns.heatmap(cm, annot=True, fmt="g")
plt.xlabel("Predicted Values")
plt.ylabel("Actual Values")
plt.show()


print("Accuracy on training set : ", accuracy_score(y_train, pred_train4))

Accuracy on training set :  0.7811515437933207


logit_roc_auc_train = roc_auc_score(y_train, lg5.predict(X_train5))
fpr, tpr, thresholds = roc_curve(y_train, lg5.predict(X_train5))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# dropping variables from test set as well which were dropped from training set
X_test1 = X_test.drop([ 'no_of_weekend_nights_log',
                         'no_of_week_nights',
                         'market_segment_type_Online',
                         'market_segment_type_Offline',
                         'market_segment_type_Corporate',
                        'market_segment_type_Complementary',
                       'room_type_reserved_Room_Type 3',
                       'room_type_reserved_Room_Type 4',
                       'no_of_previous_bookings_not_canceled_log',
                       'room_type_reserved_Room_Type 2'
                      
                     ], axis=1)


pred_test = lg5.predict(X_test1) > 0.5
pred_test = np.round(pred_test)
print("Accuracy on training set : ", accuracy_score(y_train, pred_train4))
print("Accuracy on test set : ", accuracy_score(y_test, pred_test))

Accuracy on training set :  0.7811515437933207
Accuracy on test set :  0.7846182118901038


tree_data = dummy_data.astype(float)


tree_data = tree_data.drop(['arrival_date','arrival_year','no_of_week_nights',
'no_of_weekend_nights_log'  ], axis=1)


X = tree_data.drop("booking_status" , axis=1)
y = tree_data.pop("booking_status")


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.30, random_state=1)


# Building a decision tree using the dtclassifier function
dTree = DecisionTreeClassifier(criterion = 'gini', random_state=1)
dTree.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)

DecisionTreeClassifier(random_state=1)


#scoring the accuracy on train & test data
print("Accuracy on training set : ",dTree.score(X_train, y_train))
print("Accuracy on test set : ",dTree.score(X_test, y_test))

Accuracy on training set :  0.9924385633270322
Accuracy on test set :  0.8585867867315997


# Checking the positive outcomes
y.sum(axis = 0)

11885.0


## Function to create confusion matrix
def make_confusion_matrix(model,y_actual,labels=[1, 0]):
    '''
    model : classifier to predict values of X
    y_actual : ground truth  
    
    '''
    y_predict = model.predict(X_test)
    cm=metrics.confusion_matrix( y_actual, y_predict, labels=[0, 1])
    df_cm = pd.DataFrame(cm, index = [i for i in ["Actual - No","Actual - Yes"]],
                  columns = [i for i in ['Predicted - No','Predicted - Yes']])
    group_counts = ["{0:0.0f}".format(value) for value in
                cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in
                         cm.flatten()/np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in
              zip(group_counts,group_percentages)]
    labels = np.asarray(labels).reshape(2,2)
    plt.figure(figsize = (10,7))
    sns.heatmap(df_cm, annot=labels,fmt='')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


the_features = list(X.columns)
print(the_features)

['no_of_adults', 'required_car_parking_space', 'lead_time', 'arrival_month', 'repeated_guest', 'avg_price_per_room', 'length_stay', 'no_of_children_log', 'no_of_previous_cancellations_log', 'no_of_previous_bookings_not_canceled_log', 'no_of_special_requests_log', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Meal Plan 3', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 3', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Complementary', 'market_segment_type_Corporate', 'market_segment_type_Offline', 'market_segment_type_Online']


plt.figure(figsize=(20,30))
tree.plot_tree(dTree,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


print(tree.export_text(dTree,feature_names=the_features,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests_log <= 0.35
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |--- avg_price_per_room <= 201.50
|   |   |   |   |   |   |--- lead_time <= 74.50
|   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 57.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  57.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 151.59
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  151.59
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 138.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  138.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [169.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 50.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  50.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |--- lead_time >  74.50
|   |   |   |   |   |   |   |--- lead_time <= 78.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.47
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [26.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.47
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 30.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  78.50
|   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [110.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 86.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  86.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.75
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 63.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  63.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.75
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 73.53
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  73.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |--- avg_price_per_room >  201.50
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1.0
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |--- avg_price_per_room <= 115.50
|   |   |   |   |   |   |--- length_stay <= 14.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 75.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  75.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.42
|   |   |   |   |   |   |   |   |   |--- weights: [34.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.42
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.42
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- length_stay >  14.50
|   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |--- avg_price_per_room >  115.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- length_stay <= 10.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 43.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  10.00
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- avg_price_per_room <= 75.07
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 104.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  104.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 61.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  61.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  75.07
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [23.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [50.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 86.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  86.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.50
|   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 59.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 108.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 101.12
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  101.12
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 47.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  108.50
|   |   |   |   |   |   |   |   |--- lead_time <= 104.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 177.83
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [45.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  177.83
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  104.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 110.86
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  110.86
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |--- avg_price_per_room <= 122.00
|   |   |   |   |   |   |   |--- weights: [141.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  122.00
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 123.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 82.88
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  82.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  123.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 122.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 63.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  63.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  122.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [65.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.75
|   |   |   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 138.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  138.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.75
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.41
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.41
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [60.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 202.67
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [67.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |--- length_stay <= 12.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.35
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.40
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.40
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.35
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 178.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  178.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  12.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- avg_price_per_room <= 99.38
|   |   |   |   |   |   |   |--- avg_price_per_room <= 78.90
|   |   |   |   |   |   |   |   |--- length_stay <= 15.00
|   |   |   |   |   |   |   |   |   |--- length_stay <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [84.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  15.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  78.90
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [42.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  99.38
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 119.25
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  119.25
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 160.17
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [41.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  160.17
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  202.67
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [0.00, 32.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- avg_price_per_room <= 105.27
|   |   |   |   |   |--- avg_price_per_room <= 60.07
|   |   |   |   |   |   |--- lead_time <= 84.50
|   |   |   |   |   |   |   |--- lead_time <= 51.50
|   |   |   |   |   |   |   |   |--- lead_time <= 50.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 21.67
|   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  21.67
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 49.84
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  49.84
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- lead_time >  50.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  51.50
|   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  84.50
|   |   |   |   |   |   |   |--- lead_time <= 87.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  87.50
|   |   |   |   |   |   |   |   |--- length_stay <= 8.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 59.43
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  59.43
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  8.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- avg_price_per_room >  60.07
|   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [29.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.16
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.16
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [54.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.00
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 35.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  105.27
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 195.30
|   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 38.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  38.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 135.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  135.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |--- avg_price_per_room >  195.30
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 92.00] class: 1.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- lead_time <= 22.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [22.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  22.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 168.06
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 147.75
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  147.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  168.06
|   |   |   |   |   |   |   |   |   |--- length_stay <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 80.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  80.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- length_stay <= 11.00
|   |   |   |   |   |   |   |--- weights: [39.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  11.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |--- no_of_special_requests_log >  0.35
|   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |   |--- length_stay <= 15.00
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [848.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [27.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.35
|   |   |   |   |   |   |   |   |   |   |--- weights: [43.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_children_log >  0.35
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 35.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  35.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  15.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |   |--- lead_time <= 104.50
|   |   |   |   |   |   |   |--- lead_time <= 103.50
|   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.35
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_children_log >  0.35
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  103.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  104.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 141.75
|   |   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [27.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  141.75
|   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |--- lead_time <= 63.00
|   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |--- weights: [18.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  63.00
|   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- length_stay <= 14.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 219.86
|   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.64
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.64
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 158.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  158.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  219.86
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 237.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  237.25
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- length_stay >  14.00
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 123.60
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.76
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.76
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 91.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  91.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- weights: [95.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  123.60
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 124.05
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  124.05
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 128.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  128.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 127.62
|   |   |   |   |   |   |   |--- lead_time <= 43.50
|   |   |   |   |   |   |   |   |--- length_stay <= 9.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [87.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [127.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 29.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.22
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  29.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  43.50
|   |   |   |   |   |   |   |   |--- length_stay <= 10.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.54
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.54
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- length_stay >  10.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |--- avg_price_per_room >  127.62
|   |   |   |   |   |   |   |--- lead_time <= 142.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 179.62
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  179.62
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 139.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  139.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [49.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- lead_time >  142.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 142.65
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.00
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  142.65
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 182.49
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  182.49
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |   |   |--- weights: [180.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |--- no_of_special_requests_log >  0.90
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- length_stay <= 12.00
|   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |--- weights: [1689.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.05
|   |   |   |   |   |   |   |   |   |--- lead_time <= 48.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [61.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  48.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.05
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [221.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 28.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 31.00
|   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  31.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 159.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  159.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.33
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.95
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.95
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.33
|   |   |   |   |   |   |   |   |   |--- lead_time <= 80.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  80.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |--- lead_time <= 9.00
|   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  9.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 34.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.24
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- lead_time >  34.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 72.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  72.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |--- weights: [69.00, 0.00] class: 0.0
|   |   |   |   |--- length_stay >  12.00
|   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- avg_price_per_room <= 202.95
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.33
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.37
|   |   |   |   |   |   |   |   |   |   |--- weights: [22.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.37
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 98.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  98.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.33
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 115.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  115.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.35
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 142.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  142.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_children_log >  0.35
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 105.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  105.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 103.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  103.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |--- avg_price_per_room <= 90.42
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 107.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.52
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.52
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  107.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 101.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  101.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 104.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  104.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- avg_price_per_room >  90.42
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 153.15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.60
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.60
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  153.15
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |--- weights: [52.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  202.95
|   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests_log <= 0.35
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1.0
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- lead_time <= 341.00
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [61.00, 6.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 231.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  231.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  341.00
|   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 402.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 381.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  381.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 2.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  402.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.33
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |--- avg_price_per_room <= 84.58
|   |   |   |   |   |   |--- lead_time <= 244.00
|   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |--- lead_time <= 166.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  166.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 229.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.34
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.34
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  229.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 27.07
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  27.07
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  244.00
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.83
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.83
|   |   |   |   |   |   |   |   |   |--- lead_time <= 292.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  292.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 23.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [37.00, 0.00] class: 0.0
|   |   |   |   |   |--- avg_price_per_room >  84.58
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 316.00
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  316.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 338.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  338.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 5.00] class: 1.0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |--- lead_time <= 285.50
|   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  285.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [0.00, 525.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |--- lead_time <= 204.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  204.00
|   |   |   |   |   |   |   |   |--- lead_time <= 214.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  214.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 275.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  275.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 80.51
|   |   |   |   |   |   |   |   |--- weights: [0.00, 41.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  80.51
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.43
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.43
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 13.00] class: 1.0
|   |   |--- no_of_special_requests_log >  0.35
|   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- lead_time <= 152.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.81
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.81
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  152.50
|   |   |   |   |   |   |   |   |--- lead_time <= 156.50
|   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  156.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 87.12
|   |   |   |   |   |   |   |   |--- lead_time <= 158.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  158.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  87.12
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.75
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.75
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- no_of_adults <= 0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- no_of_adults >  0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 93.44
|   |   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 162.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 161.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  161.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  162.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [52.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.38
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.92
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.92
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  93.44
|   |   |   |   |   |   |   |   |--- lead_time <= 178.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 93.67
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  93.67
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time >  178.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 179.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  179.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 1.00] class: 0.0
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |--- lead_time <= 187.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.30
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.30
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 20.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  187.50
|   |   |   |   |   |   |   |   |--- lead_time <= 304.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.90
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 237.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  237.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.90
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |--- lead_time >  304.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1.0
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |--- length_stay <= 13.50
|   |   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.32
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 247.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  247.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.32
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.89
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.89
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  13.50
|   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1.0
|   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |--- lead_time <= 368.00
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |--- length_stay <= 7.50
|   |   |   |   |   |   |   |   |--- lead_time <= 331.00
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |   |   |   |   |   |   |   |--- weights: [137.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  0.90
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  331.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 336.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  336.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.74
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.74
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |--- lead_time <= 196.00
|   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  196.00
|   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  0.90
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- avg_price_per_room <= 58.50
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  58.50
|   |   |   |   |   |   |   |--- weights: [6.00, 2.00] class: 0.0
|   |   |   |   |--- lead_time >  368.00
|   |   |   |   |   |--- lead_time <= 381.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  381.50
|   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |--- weights: [0.00, 2108.00] class: 1.0
|   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |--- weights: [31.00, 0.00] class: 0.0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests_log <= 0.35
|   |   |   |   |--- weights: [47.00, 0.00] class: 0.0
|   |   |   |--- no_of_special_requests_log >  0.35
|   |   |   |   |--- lead_time <= 289.50
|   |   |   |   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |   |   |   |--- avg_price_per_room <= 114.59
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  114.59
|   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |--- no_of_special_requests_log >  0.90
|   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 110.46
|   |   |   |   |   |   |   |   |--- lead_time <= 206.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  206.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  110.46
|   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |--- lead_time >  289.50
|   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0


# checking out what variables are being prioritized by the model.

print (pd.DataFrame(dTree.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                             Imp
lead_time                                0.39708
avg_price_per_room                       0.20728
market_segment_type_Online               0.09275
arrival_month                            0.08443
length_stay                              0.07326
no_of_special_requests_log               0.06831
no_of_adults                             0.02970
type_of_meal_plan_Not Selected           0.01111
room_type_reserved_Room_Type 4           0.00820
required_car_parking_space               0.00738
no_of_children_log                       0.00590
type_of_meal_plan_Meal Plan 2            0.00456
market_segment_type_Offline              0.00352
room_type_reserved_Room_Type 2           0.00224
room_type_reserved_Room_Type 5           0.00171
room_type_reserved_Room_Type 6           0.00075
market_segment_type_Corporate            0.00069
repeated_guest                           0.00047
room_type_reserved_Room_Type 7           0.00034
no_of_previous_cancellations_log         0.00032
room_type_reserved_Room_Type 3           0.00000
market_segment_type_Complementary        0.00000
no_of_previous_bookings_not_canceled_log 0.00000
type_of_meal_plan_Meal Plan 3            0.00000


importances = dTree.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


# Pre prune the model with max depth hyperparameter
dTree1 = DecisionTreeClassifier(criterion = 'gini',max_depth=3,random_state=1)
dTree1.fit(X_train, y_train)

DecisionTreeClassifier(max_depth=3, random_state=1)

DecisionTreeClassifier(max_depth=3, random_state=1)


# The accuracy on the pre pruned tree.
print("Accuracy on training set : ",dTree1.score(X_train, y_train))
print("Accuracy on test set : ",dTree1.score(X_test, y_test))

Accuracy on training set :  0.7844202898550725
Accuracy on test set :  0.7913259211614444


# Let's see the pre pruned tree
plt.figure(figsize=(15,10))

tree.plot_tree(dTree1,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()


print(tree.export_text(dTree1,feature_names=the_features,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests_log <= 0.35
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- weights: [4614.00, 781.00] class: 0.0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- weights: [2504.00, 2768.00] class: 1.0
|   |--- no_of_special_requests_log >  0.35
|   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |--- weights: [5624.00, 1055.00] class: 0.0
|   |   |--- no_of_special_requests_log >  0.90
|   |   |   |--- weights: [2919.00, 145.00] class: 0.0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests_log <= 0.35
|   |   |   |--- weights: [694.00, 1242.00] class: 1.0
|   |   |--- no_of_special_requests_log >  0.35
|   |   |   |--- weights: [586.00, 249.00] class: 0.0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- weights: [31.00, 2108.00] class: 1.0
|   |   |--- arrival_month >  11.50
|   |   |   |--- weights: [57.00, 15.00] class: 0.0


# Looking at the feature importances of this model
importances = dTree1.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(10,10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()


clf = DecisionTreeClassifier(random_state=1, class_weight="balanced")
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities


pd.DataFrame(path)


fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()


clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
    )
    clf.fit(X_train, y_train)
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.08117914389136888


clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()


f1_train = []
for clf in clfs:
    pred_train = clf.predict(X_train)
    values_train = f1_score(y_train, pred_train)
    f1_train.append(values_train)

f1_test = []
for clf in clfs:
    pred_test = clf.predict(X_test)
    values_test = f1_score(y_test, pred_test)
    f1_test.append(values_test)


fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("F1 Score")
ax.set_title("F1 Score vs alpha for training and testing sets")
ax.plot(ccp_alphas, f1_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, f1_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()


index_best_model = np.argmax(f1_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=6.68270542106583e-05, class_weight='balanced',
                       random_state=1)


comparison_frame = pd.DataFrame({'Model':['Initial decision tree model','Decision tree with restricted maximum depth','Decision treee with hyperparameter tuning',
                                         'Decision tree with post-pruning'], 'Train_Recall':[.981,.732,.732,.979], 'Test_Recall':[.792,.739,.739,.794]}) 
comparison_frame

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests
count	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000	36275.00000
mean	1.84496	0.10528	0.81072	2.20430	0.03099	85.23256	2017.82043	7.42365	15.59700	0.02564	0.02335	0.15341	103.42354	0.61966
std	0.51871	0.40265	0.87064	1.41090	0.17328	85.93082	0.38384	3.06989	8.74045	0.15805	0.36833	1.75417	35.08942	0.78624
min	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	2017.00000	1.00000	1.00000	0.00000	0.00000	0.00000	0.00000	0.00000
25%	2.00000	0.00000	0.00000	1.00000	0.00000	17.00000	2018.00000	5.00000	8.00000	0.00000	0.00000	0.00000	80.30000	0.00000
50%	2.00000	0.00000	1.00000	2.00000	0.00000	57.00000	2018.00000	8.00000	16.00000	0.00000	0.00000	0.00000	99.45000	0.00000
75%	2.00000	0.00000	2.00000	3.00000	0.00000	126.00000	2018.00000	10.00000	23.00000	0.00000	0.00000	0.00000	120.00000	1.00000
max	4.00000	10.00000	7.00000	17.00000	1.00000	443.00000	2018.00000	12.00000	31.00000	1.00000	13.00000	58.00000	540.00000	5.00000

	ccp_alphas	impurities
0	0.00000	0.01030
1	0.00000	0.01030
2	0.00000	0.01030
3	0.00000	0.01030
4	0.00000	0.01030
...	...	...
2055	0.00890	0.32806
2056	0.00980	0.33786
2057	0.01272	0.35058
2058	0.03412	0.41882
2059	0.08118	0.50000

	Model	Train_Recall	Test_Recall
0	Initial decision tree model	0.98100	0.79200
1	Decision tree with restricted maximum depth	0.73200	0.73900
2	Decision treee with hyperparameter tuning	0.73200	0.73900
3	Decision tree with post-pruning	0.97900	0.79400

INN Hotels Project¶

Context¶

Objective¶

Data Description¶

Importing necessary libraries and data¶

Data Overview¶

Observations¶

Observations¶

Exploratory Data Analysis (EDA)¶

What are the busiest months in the hotel?¶

The busiest 4 months are:¶

Which market segment do most of the guests come from?¶

What are the differences in room prices in different market segments?¶

What percentage of bookings are canceled?¶

What percentage of repeating guests cancel?¶

Do special requirements affect booking cancellation?¶

Univariate¶

Data Preprocessing¶

EDA Insights¶

Checking Multicollinearity¶

Building a Logistic Regression model¶

Model can make wrong predictions as:¶

How to reduce these costs and maximize True Positives?¶

Model performance evaluation¶

Final Model Summary¶

Building a Decision Tree model¶

Do we need to prune our Decision Tree¶

Model Performance Comparison and Conclusions¶

Actionable Insights and Recommendations¶

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	1	2	Meal Plan 1	Room_Type 1	224	2017	10	2	Offline	65.00000	0	Not_Canceled
1	INN00002	2	2	3	Not Selected	Room_Type 1	5	2018	11	6	Online	106.68000	1	Not_Canceled
2	INN00003	1	2	1	Meal Plan 1	Room_Type 1	1	2018	2	28	Online	60.00000	0	Canceled
3	INN00004	2	0	2	Meal Plan 1	Room_Type 1	211	2018	5	20	Online	100.00000	0	Canceled
4	INN00005	2	1	1	Not Selected	Room_Type 1	48	2018	4	11	Online	94.50000	0	Canceled

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
36270	INN36271	3	2	6	Meal Plan 1	Room_Type 4	85	2018	8	3	Online	167.80000	1	Not_Canceled
36271	INN36272	2	1	3	Meal Plan 1	Room_Type 1	228	2018	10	17	Online	90.95000	2	Canceled
36272	INN36273	2	2	6	Meal Plan 1	Room_Type 1	148	2018	7	1	Online	98.39000	2	Not_Canceled
36273	INN36274	2	0	3	Not Selected	Room_Type 1	63	2018	4	21	Online	94.50000	0	Canceled
36274	INN36275	2	1	2	Meal Plan 1	Room_Type 1	207	2018	12	30	Offline	161.67000	0	Not_Canceled

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
9231	INN09232	2	2	2	Not Selected	Room_Type 1	208	2018	7	30	Online	80.75000	0	Canceled
20113	INN20114	2	0	2	Meal Plan 1	Room_Type 1	48	2018	11	4	Online	97.20000	2	Not_Canceled
23076	INN23077	2	1	3	Meal Plan 1	Room_Type 4	120	2018	8	8	Online	127.80000	0	Canceled
14762	INN14763	3	1	2	Meal Plan 2	Room_Type 4	59	2018	4	22	Online	189.00000	3	Not_Canceled
28058	INN28059	1	0	2	Meal Plan 1	Room_Type 1	151	2018	1	19	Offline	71.00000	0	Not_Canceled

	type_of_meal_plan	room_type_reserved	market_segment_type	booking_status
count	36275	36275	36275	36275
unique	4	7	5	2
top	Meal Plan 1	Room_Type 1	Online	Not_Canceled
freq	27835	28130	23214	24390

	avg_price_per_room
market_segment_type
Online	112.25685
Aviation	100.70400
Offline	91.63268
Corporate	82.91174
Complementary	3.14176

	no_of_adults	no_of_week_nights	lead_time	arrival_year	arrival_month	arrival_date	avg_price_per_room	booking_status	length_stay	no_of_weekend_nights_log	no_of_special_requests_log	type_of_meal_plan_Not Selected	market_segment_type_Offline	market_segment_type_Online
0	2	2	224	2017	10	2	65.00000	False	3	0.69315	0.00000	0	1	0
1	2	3	5	2018	11	6	106.68000	False	5	1.09861	0.69315	1	0	1
2	1	1	1	2018	2	28	60.00000	True	3	1.09861	0.00000	0	0	1
3	2	2	211	2018	5	20	100.00000	True	2	0.00000	0.00000	0	0	1
4	2	1	48	2018	4	11	94.50000	True	2	0.69315	0.00000	1	0	1